Extreme-Point-based Heuristics for the Three-Dimensional Bin Packing problem

One of the main issues in addressing three-dimensional packing problems is finding an efficient and accurate definition of the points at which to place the items inside the bins, because the performance of exact and heuristic solution methods is actually strongly influenced by the choice of a placement rule. We introduce the extreme point concept and present a new extreme point-based rule for packing items inside a three-dimensional container. The extreme point rule is independent from the particular packing problem addressed and can handle additional constraints, such as fixing the position of the items. The new extreme point rule is also used to derive new constructive heuristics for the three-dimensional bin-packing problem. Extensive computational results show the effectiveness of the new heuristics compared to state-of-the-art results. Moreover, the same heuristics, when applied to the two-dimensional bin-packing problem, outperform those specifically designed for the proble

Expected sensitivity of ARGO-YBJ to detect point gamma-ray sources

ARGO-YBJ is a full coverage air shower detector currently under construction at the Yangbajing Laboratory (4300 m a.s.l., Tibet, China). First data obtained with a subset of the apparatus will be available in summer 2003 while the full detector operation is expected in 2005. One of the main aims of ARGO-YBJ is the observation of gamma-ray sources, at an energy threshold of a few hundreds GeV. In this paper we present the expected sensitivity to detect point gamma ray sources, with particular attention to the Crab Nebula. According to our simulations a Crab-like signal could be detected in one year of operation with a statistical significance of 10 standard deviations, without any gamma/hadron discrimination.Comment: 4 pages, 2 Postscript figure

Algorithms for minimizing maximum lateness with unit length tasks and resource constraints

AbstractThe problem we consider is that of scheduling n unit length tasks on identical processors in the presence of additional scarce resources. The objective is to minimize maximum lateness. It has been known for some time that the problem is NP-hard even for two processors and one resource type. In the present paper we show that the problem can be solved in O(n log n) time, even for an arbitrary number of resources if the instance of the problem has the saturation property (i.e., no resource unit is idle in an optimal schedule). For the more general problem without saturation, two heuristic algorithms and a branch and bound approach are proposed. The results of computational tests of the above methods are also reported

A robust enhancement to the Clarke-Wright savings algorithm

We address the Clarke and Wright (CW) savings algorithm proposed for the Capacitated Vehicle Routing Problem (CVRP). We first consider a recent enhancement which uses the put first larger items idea originally proposed for the bin packing problem and show that the conflicting idea of putting smaller items first has a comparable performance. Next, we propose a robust enhancement to the CW savings formulation. The proposed formulation is normalized to efficiently solve different problems, independent from the measurement units and parameter intervals. To test the performance of the proposed savings function, we conduct an extensive computational study on a large set of well-known instances from the literature. Our results show that the proposed savings function provides shorter distances in the majority of the instances and the average performance is significantly better than previously presented enhancements

Naming and Framing Service Learning: A Taxonomy and Four Levels of Value

Service learning draws upon some of the noblest intentions of American higher education: its goal is to develop an educated and engaged citizenry willing and able to address society\u27s ills. This idea of service learning resonates deeply with the felt needs of our time, and perhaps nowhere more keenly than in the urban locations of metropolitan colleges and universities. Consequently, service learning is perhaps the fastest growing and the most promising movement within higher education today

Cervial cancer screening among HIV-positive women in rural Cambodia: a pilot programme

Mexico AIDS Conference 200

Infrared thermography as a tool to evaluate body surface temperature and its relationship with feed efficiency in Bos indicus cattle in tropical conditions.

201

A SAT encoding for Multi-dimensional Packing Problems

International audienceThe Orthogonal Packing Problem (OPP) consists in determining if a set of items can be packed into a given container. This decision problem is NP-complete. S. P. Fekete et al. modelled the problem in which the overlaps between the objects in each dimension are represented by interval graphs. In this paper we propose a SAT encoding of Fekete et al. characterization. Some results are presented, and the efficiency of this approach is compared with other SAT encodings

Lagrangian matheuristics for the Quadratic Multiple Knapsack Problem

The Quadratic Multiple Knapsack Problem (QMKP) is a challenging combinatorial optimization problem combining the well-known Quadratic Knapsack Problem with the Multiple Knapsack Problem. After a long stream of research devoted to metaheuristic approaches for large-scale instances, only recently some authors started to study the mathematical properties of the QMKP and proposed exact solution methods for benchmark instances of smaller size. In this paper, we propose the first matheuristic approach for solving the QMKP approximately. The proposed method exploits the strength of a Lagrangian relaxation for the natural quadratic formulation of the QMKP to derive heuristic solutions. Postoptimization local search procedures are embedded in the final framework. Experimental studies show that the resulting deterministic matheuristic approach consistently delivers solutions of very good quality in short computing times

The Networked Common Goods Game

We introduce a new class of games called the networked common goods game (NCGG), which generalizes the well-known common goods game. We focus on a fairly general subclass of the game where each agent's utility functions are the same across all goods the agent is entitled to and satisfy certain natural properties (diminishing return and smoothness). We give a comprehensive set of technical results listed as follows. * We show the optimization problem faced by a single agent can be solved efficiently in this subclass. The discrete version of the problem is however NP-hard but admits an fully polynomial time approximation scheme (FPTAS). * We show uniqueness results of pure strategy Nash equilibrium of NCGG, and that the equilibrium is fully characterized by the structure of the network and independent of the choices and combinations of agent utility functions. * We show NCGG is a potential game, and give an implementation of best/better response Nash dynamics that lead to fast convergence to an $\epsilon$-approximate pure strategy Nash equilibrium. * Lastly, we show the price of anarchy of NCGG can be as large as $\Omega(n^{1-\epsilon})$ (for any $\epsilon>0$), which means selfish behavior in NCGG can lead to extremely inefficient social outcomes